OpenCV颜色识别实例(所有颜色均可识别)
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本文中的颜色识别为红色,颜色阈值设置如下:
lower_apple = np.array([0, 100, 100])
higher_apple = np.array([10, 200, 200])
识别其他颜色可以参考HSV颜色阈值设置进行更改
下面是识别代码,注释很详细:
import cv2
import numpy as npdef red_identify(img):# 灰度图gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)# 转换为HSVhsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)# 二值化处理lower_apple = np.array([0, 100, 100])higher_apple = np.array([10, 200, 200])mask = cv2.inRange(hsv, lower_apple, higher_apple)# 膨胀操作kernel = np.ones([5, 5], dtype=np.uint8)dilate = cv2.dilate(mask, kernel, iterations=1)# 画出轮廓cnts, hierarchy = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)# 判断是否有轮廓if len(cnts) == 0:# 没有即显示原图cv2.imshow("red_identify", img)returnmax_cnt = max(cnts, key=cv2.contourArea)cv2.drawContours(img, max_cnt, -1, (0, 0, 255), 2)# 最大外接矩形(x, y, w, h) = cv2.boundingRect(max_cnt)cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 3)cv2.imshow("red_identify", img)if __name__ == "__main__":# 打开摄像头cap = cv2.VideoCapture(0)# 设置摄像头参数,3和4为像素大小,5为帧率cap.set(3, 256)cap.set(4, 256)cap.set(5, 60)while True:# 循环读取每一帧flag, frame = cap.read()# 读取失败if not flag:print("Camera error!")break# 调用颜色识别red_identify(frame)# 若没有按下q键,则每10毫秒显示一帧(OxFF为"q"的ASCII码)if cv2.waitKey(10) & 0xFF == ord('q'):breakcap.release()cv2.destroyAllWindows()
下面是识别效果: